Adaptive Critical Care Intervention in the Internet of Medical Things

The possibility of continuous monitoring of health conditions represents a crucial aspect for the improvement of living conditions, the prevention of potential pathologies and prompt response in critical situations. In particular, in intensive care or emergency situations, the evaluation of illness degree of a clinical risk level can be considered a predictive task in situations where streams of vital signs data are gathered by medical devices and the Internet of Medical Things (IoMT) sensors. In this framework, Early Warning Score (EWS) systems generating an aggregate score based on the measurement of a set of vital signs, such as National Early Warning Score 2 (NEWS2) and Modified Early Warning Score (MEWS), may provide a helpful decision support for the estimation of health state and triggers for critical care intervention. In the present work, we address a preliminary analysis in order to investigate the most suitable Machine Learning (ML) technique for the prediction of clinical risk classes of a continuously monitored patient in a particular condition where a limited number of vital parameters is available. This analysis is then intended to be preparatory for the final goal of designing an edge device connected to one or more wearable medical devices via IoMT, which adaptively exploits the best ML model to predict a reliable EWS.